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Found 14 papers, 2 papers with code
Robust Energy Consumption Prediction with a Missing Value-Resilient Metaheuristic-based Neural Network in Mobile App Development
Energy consumption is a fundamental concern in mobile application development, bearing substantial significance for both developers and end-users.
A Metaheuristic-based Machine Learning Approach for Energy Prediction in Mobile App Development
Energy consumption plays a vital role in mobile App development for developers and end-users, and it is considered one of the most crucial factors for purchasing a smartphone.
A Cluster-Based Opposition Differential Evolution Algorithm Boosted by a Local Search for ECG Signal Classification
This paper employs an enhanced differential evolution (DE) algorithm for the training process as one of the most effective population-based algorithms.
A Novel Plagiarism Detection Approach Combining BERT-based Word Embedding, Attention-based LSTMs and an Improved Differential Evolution Algorithm
In this article, we propose a novel method for detecting plagiarism that is based on attention mechanism-based long short-term memory (LSTM) and bidirectional encoder representations from transformers (BERT) word embedding, enhanced with optimized differential evolution (DE) method for pre-training and a focal loss function for training.
Metaheuristic-based Energy-aware Image Compression for Mobile App Development
To this end, we propose a novel objective function for population-based JPEG image compression.
Energy-Aware JPEG Image Compression: A Multi-Objective Approach
Also, two Pareto-based methods, including a non-dominated sorting genetic algorithm (NSGA-II) and a reference-point-based NSGA-II (NSGA-III) are used for the embedding scheme, and two Pareto-based algorithms, EnNSGAII and EnNSGAIII, are presented.
Machine Learning Testing in an ADAS Case Study Using Simulation-Integrated Bio-Inspired Search-Based Testing
This paper presents an extended version of Deeper, a search-based simulation-integrated test solution that generates failure-revealing test scenarios for testing a deep neural network-based lane-keeping system.
MCS-HMS: A Multi-Cluster Selection Strategy for the Human Mental Search Algorithm
This is not necessarily the best criterion to choose the winner group and limits the exploration ability of the algorithm.
HMS-OS: Improving the Human Mental Search Optimisation Algorithm by Grouping in both Search and Objective Space
The human mental search (HMS) algorithm is a relatively recent population-based metaheuristic algorithm, which has shown competitive performance in solving complex optimisation problems.
An LSTM-based Plagiarism Detection via Attention Mechanism and a Population-based Approach for Pre-Training Parameters with imbalanced Classes
To this end, this paper employs a PBMH algorithm, artificial bee colony (ABC), to moderate the problem.
An Enhanced Differential Evolution Algorithm Using a Novel Clustering-based Mutation Operator
Differential evolution (DE) is an effective population-based metaheuristic algorithm for solving complex optimisation problems.
Ensemble of Loss Functions to Improve Generalizability of Deep Metric Learning methods
The proposed ensemble of losses enforces the deep model to extract features that are consistent with all losses.
Differential Evolution-based Neural Network Training Incorporating a Centroid-based Strategy and Dynamic Opposition-based Learning
This improves exploitation since the new member is obtained based on the best individuals, while the employed DOBL strategy, which uses the opposite of an individual, leads to enhanced exploration.
Towards Solving Large-scale Expensive Optimization Problems Efficiently Using Coordinate Descent Algorithm
To the best our knowledge, there is no significant study to assess benchmark functions with various dimensions and landscape properties to investigate CD algorithm.
